In x-ray computed tomography (CT), noise and stray radiation may cause significant artifacts in the reconstructed tomogram or digital volume model of the object. A number of methods for correcting stray beams use the difference-based approach, in which measurements or simulations are used to establish which stray radiation artifacts are situated in the individual projection image data records. The measured or simulated stray signal, i.e. the stray component in the projections, is then subtracted from the individual projection images. However, the stray radiation corrected projection images have a lower signal-to-noise ratio (SNR) and lead to significantly larger image noise in the subsequent reconstruction, i.e. during calculation of a tomogram or volume model. The described, subtraction-based a posteriori correction of the stray radiation may, for example, be based on a Monte Carlo simulation, first order deterministic calculations of the radiation or convolution algorithms based on point spread functions. By way of example, the distribution of the stray component on the radiation detector may be measured on the basis of phantom bodies.
The known methods are disadvantageous in that only the large area, i.e. spatially low-frequency, stray signal can be estimated and the signal-to-noise ratio becomes worse, i.e. reduces, in the corrected projections as a result of forming the difference between the measured signal and stray signal. This has a visible effect on the reconstructed tomogram or volume model, as a result of which the detailed identifiability of structures in the imaged object may be lost. This structure information may include important primary information, which should be maintained when the stray radiation artifacts are reduced in a tomogram or volume rendering of the object.